Abstract
The deluge of single-cell data obtained by sequencing, imaging and epigenetic markers has led to an increasingly detailed description of cell state. However, it remains challenging to identify how cells transition between different states, in part because data are typically limited to snapshots in time. A prerequisite for inferring cell state transitions from such snapshots is to distinguish whether transitions are coupled to cell divisions. To address this, we present two minimal branching process models of cell division and differentiation in a well-mixed population. These models describe dynamics where differentiation and division are coupled or uncoupled. For each model, we derive analytic expressions for each subpopulation’s mean and variance and for the likelihood, allowing exact Bayesian parameter inference and model selection in the idealised case of fully observed trajectories of differentiation and division events. In the case of snapshots, we present a sample path algorithm and use this to predict optimal temporal spacing of measurements for experimental design. We then apply this methodology to an in vitro dataset assaying the clonal growth of epiblast stem cells in culture conditions promoting self-renewal or differentiation. Here, the larger number of cell states necessitates approximate Bayesian computation. For both culture conditions, our inference supports the model where cell state transitions are coupled to division. For culture conditions promoting differentiation, our analysis indicates a possible shift in dynamics, with these processes becoming more coupled over time.
Highlights
Changes in gene expression underlie many aspects of cellular behaviour in tissue development, homeostasis, and regeneration
We begin this section by introducing two continuoustime Markov processes as minimal stochastic models for stem cell dynamics. These simple models are generalized to describe the dynamics of epiblast stem cells (EpiSC) in more detail
Neglecting cases for which A-state populations decrease in model C, mean populations grow exponentially over long time scales in both models with rates depending on the underlying model parameters
Summary
Changes in gene expression underlie many aspects of cellular behaviour in tissue development, homeostasis, and regeneration. The concept of discrete cell states is intended to capture the distinct patterns of gene expression that are observed within tissues over time. There are various ways to interrogate a cell’s state in different contexts in vivo and in vitro. Modern technologies such as scRNAseq produce vast amounts of data but are costly, laborious to analyse, and relatively noisy. Older techniques, such as immunofluorescent stainings, where cell state can be defined by the co-expression of a small number of genes and/or proteins, are cheaper and simpler and still remain heavily used
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